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cs.CV2026
Fine-Grained Multi Image Object Hallucination Benchmark
Joonki Min, Chaeyun Kim, Hyungwook Choi +4
Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundam…
cs.CV2026
A More Word-like Image Tokenization for MLLMs
Hyun Lee, Hyemin Jeong, Yejin Kim +4
Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a sequence of tokens in its embedding…
cs.CV2026
Enhancing Multi-Image Understanding through Delimiter Token Scaling
Minyoung Lee, Yeji Park, Dongjun Hwang +3
Large Vision-Language Models (LVLMs) achieve strong performance on single-image tasks, but their performance declines when multiple images are provided as input. One major reason i…